void Testmonitor_acf(CuTest* tc) {
x = gsl_vector_alloc(1);
y = gsl_vector_alloc(1);
m = mcmclib_monitor_acf_alloc(1, MAX_LAG);
ACF = gsl_matrix_alloc(MAX_LAG+1, 1);
append(1.0);
append(-1.0);
CuAssertDblEquals(tc, 1.0, acf(0), TOL);
append(1.0);
double mean = 1.0/3.0;
double var = 1.0 - mean*mean;
CuAssertDblEquals(tc, var, acf(0), TOL);
double acf_check = -2.0/3.0;
CuAssertDblEquals(tc, acf_check, acf(1), TOL);
append(-1.0);
mean = 0.0;
var = 1.0;
CuAssertDblEquals(tc, 1.0, acf(0), TOL);
CuAssertDblEquals(tc, -3.0/4.0, acf(1), TOL);
gsl_vector* iact = gsl_vector_alloc(1);
mcmclib_monitor_acf_get(m, ACF);
mcmclib_iact_from_acf(ACF, iact);
CuAssertDblEquals(tc, -0.5, gsl_vector_get(iact, 0), TOL);
gsl_vector_free(iact);
gsl_matrix_free(ACF);
mcmclib_monitor_acf_free(m);
gsl_vector_free(x);
gsl_vector_free(y);
}
void
TempoTrackV2::get_rcf(const d_vec_t &dfframe_in, const d_vec_t &wv, d_vec_t &rcf)
{
// calculate autocorrelation function
// then rcf
// just hard code for now... don't really need separate functions to do this
// make acf
d_vec_t dfframe(dfframe_in);
MathUtilities::adaptiveThreshold(dfframe);
d_vec_t acf(dfframe.size());
for (unsigned int lag=0; lag<dfframe.size(); lag++)
{
double sum = 0.;
double tmp = 0.;
for (unsigned int n=0; n<(dfframe.size()-lag); n++)
{
tmp = dfframe[n] * dfframe[n+lag];
sum += tmp;
}
acf[lag] = static_cast<double> (sum/ (dfframe.size()-lag));
}
// now apply comb filtering
int numelem = 4;
for (unsigned int i = 2;i < rcf.size();i++) // max beat period
{
for (int a = 1;a <= numelem;a++) // number of comb elements
{
for (int b = 1-a;b <= a-1;b++) // general state using normalisation of comb elements
{
rcf[i-1] += ( acf[(a*i+b)-1]*wv[i-1] ) / (2.*a-1.); // calculate value for comb filter row
}
}
}
// apply adaptive threshold to rcf
MathUtilities::adaptiveThreshold(rcf);
double rcfsum =0.;
for (unsigned int i=0; i<rcf.size(); i++)
{
rcf[i] += EPS ;
rcfsum += rcf[i];
}
// normalise rcf to sum to unity
for (unsigned int i=0; i<rcf.size(); i++)
{
rcf[i] /= (rcfsum + EPS);
}
}
void GrDrawContext::discard(GrRenderTarget* renderTarget) {
RETURN_IF_ABANDONED
SkASSERT(renderTarget);
AutoCheckFlush acf(fContext);
if (!this->prepareToDraw(renderTarget)) {
return;
}
fDrawTarget->discard(renderTarget);
}
void GrDrawContext::drawPaint(GrRenderTarget* rt,
const GrClip& clip,
const GrPaint& origPaint,
const SkMatrix& viewMatrix) {
RETURN_IF_ABANDONED
// set rect to be big enough to fill the space, but not super-huge, so we
// don't overflow fixed-point implementations
SkRect r;
r.setLTRB(0, 0,
SkIntToScalar(rt->width()),
SkIntToScalar(rt->height()));
SkTCopyOnFirstWrite<GrPaint> paint(origPaint);
// by definition this fills the entire clip, no need for AA
if (paint->isAntiAlias()) {
paint.writable()->setAntiAlias(false);
}
bool isPerspective = viewMatrix.hasPerspective();
// We attempt to map r by the inverse matrix and draw that. mapRect will
// map the four corners and bound them with a new rect. This will not
// produce a correct result for some perspective matrices.
if (!isPerspective) {
SkMatrix inverse;
if (!viewMatrix.invert(&inverse)) {
SkDebugf("Could not invert matrix\n");
return;
}
inverse.mapRect(&r);
this->drawRect(rt, clip, *paint, viewMatrix, r);
} else {
SkMatrix localMatrix;
if (!viewMatrix.invert(&localMatrix)) {
SkDebugf("Could not invert matrix\n");
return;
}
AutoCheckFlush acf(fContext);
if (!this->prepareToDraw(rt)) {
return;
}
GrPipelineBuilder pipelineBuilder(*paint, rt, clip);
fDrawTarget->drawBWRect(pipelineBuilder,
paint->getColor(),
SkMatrix::I(),
r,
NULL,
&localMatrix);
}
}
void GrDrawContext::clear(GrRenderTarget* renderTarget,
const SkIRect* rect,
const GrColor color,
bool canIgnoreRect) {
RETURN_IF_ABANDONED
SkASSERT(renderTarget);
AutoCheckFlush acf(fContext);
if (!this->prepareToDraw(renderTarget)) {
return;
}
fDrawTarget->clear(rect, color, canIgnoreRect, renderTarget);
}
void omxComputeNumericDeriv::omxCalcFinalConstraintJacobian(FitContext* fc, int npar){
allconstraints_functional acf(*fc, verbose);
Eigen::MatrixWrapper< Eigen::ArrayXd > optimaM(optima);
Eigen::VectorXd resulttmp(fc->state->numEqC + fc->state->numIneqC);
Eigen::MatrixXd jactmp(fc->state->numEqC + fc->state->numIneqC, npar);
acf(optimaM, resulttmp, jactmp);
/*Gradient algorithm, iterations, and stepsize are hardcoded as they are for two reasons.
* 1. Differentiating the constraint functions should not take long, expecially compared to
* twice-differentiating the fitfunction, so it might as well be done carefully.
* 2. The default behavior during the ComputeNumericDeriv step uses different values of
* gradient stepsize and iterations depending on whether or not the MxModel contains thresholds,
* since the numerical accuracy of the -2logL is worse when multivariate-normal integration is involved.
* But, that has no bearing on the constraint functions, so it doesn't really make sense to use
* the stepsize and iterations stored in the omxComputeNumericDeriv object.
*/
fd_jacobian<true>(
GradientAlgorithm_Central, 4, 1.0e-7,
acf, resulttmp, optimaM, jactmp);
fc->constraintFunVals = resulttmp;
fc->constraintJacobian = jactmp;
return;
}
// Optimized for low memory usage
void arfit(const Matrix &x, int order, Matrix &a, Matrix &q, Matrix &v) {
if(order + 2 >= x.rows())
std::cerr << "ERROR: Not enough data. Try a smaller order." << std::endl << die();
std::vector< Matrix > acf(order + 1);
for(int p = 0; p <= order; ++p)
acf[p] = x.block(p, 0, x.rows() - p, x.cols()).transpose() * x.block(0, 0, x.rows() - p, x.cols());
std::vector< std::vector< Matrix > > vACF_0(order), vACF_1(order);
for(int row = 0; row < order; ++row) {
vACF_0[row].resize(order);
vACF_1[row].resize(order);
for(int col = 0; col < order; ++col)
if(row <= col) {
vACF_0[row][col] = acf[col-row];
vACF_1[row][col] = acf[col-row+1];
} else {
vACF_0[row][col] = acf[row-col].transpose();
vACF_1[row][col] = acf[row-col+1].transpose();
}
}
v = mcat(vACF_0);
Matrix ACF_1 = mcat(vACF_1);
Matrix A = ACF_1 * v.inverse();
Matrix Q = (v - A * ACF_1.transpose()) / x.rows();
a = A.block(0, 0, x.cols(), x.cols() * order);
q = Q.block(0, 0, x.cols(), x.cols());
v /= x.rows();
}
void posterior_summary(const gsl_matrix *theta, FILE *ofile, long M)
{
size_t T=theta->size1;
size_t npar=theta->size2;
gsl_vector *tmp=gsl_vector_alloc(T);
int i,j;
double median,lower,upper;
printf("\n Writing MCMC draws to out\n\n");
FILE *file = fopen("out","w");
for(i=0;i<T;i++){
for(j=0;j<npar;j++)
fprintf(file,"%14.6e ",mget(theta,i,j));
fprintf(file,"\n");
}
fprintf(ofile,"\n\n Posterior Summary \n");
fprintf(ofile,"\n T=%lu\n\n",T);
fprintf(ofile,"\n Mean Median Stdev 0.95 DI\n\n");
for(i=0;i<npar;i++){
gsl_matrix_get_col( tmp, theta, i);
gsl_sort_vector(tmp);
median=gsl_stats_median_from_sorted_data(tmp->data,tmp->stride,tmp->size);
lower=gsl_stats_quantile_from_sorted_data(tmp->data,tmp->stride,tmp->size,0.025);
upper=gsl_stats_quantile_from_sorted_data(tmp->data,tmp->stride,tmp->size,0.975);
fprintf(ofile,"%2d %14.7e %14.7e %14.7e (%14.7e,%14.7e)\n"
,i,mean(tmp),median,sqrt(var(tmp)),lower,upper);
}
long tau;
if( M < 0 )
tau=1000;
else
tau=M;
gsl_vector *rho=gsl_vector_alloc(tau);
fprintf(ofile,"\n ACF");
fprintf(ofile,"\n NSE Ineff 1 50 100 500\n");
for(i=0;i<npar;i++){
gsl_matrix_get_col( tmp, theta, i);
acf(tmp,tau,rho);
/* write out ACF for each parameter */
char file_name[20] = "acf.dat";
sprintf( &file_name[7],"%d",i);
FILE *fp_acf = fopen( file_name, "w");
for(j=0;j<tau;j++)
fprintf(fp_acf,"%g\n",vget(rho,j));
fclose(fp_acf);
/* get inefficiency factor using Newey-West estimate of Long-run var*/
double ineff=1.0;
for(j=0;j<tau-1;j++){
ineff += 2.0*(tau-j-1)/tau*vget(rho,j);
}
/* numerical standard error for posterior mean */
double nse=sqrt(var(tmp)*ineff/T);
fprintf(ofile,"%2d %12.5e %12.5e %12.5e %12.5e %12.5e %12.5e\n"
,i,nse,ineff,vget(rho,0),vget(rho,49),vget(rho,99),vget(rho,499));
/* produce kernel density plot for each parameter */
char file_name2[20] = "den.dat";
sprintf( &file_name2[7],"%d",i);
FILE *fp_den = fopen( file_name2, "w");
double stdev = sqrt(var(tmp));
lower = gsl_vector_min(tmp) - stdev;
upper = gsl_vector_max(tmp) + stdev;
den_est_file(tmp, lower , upper ,100, fp_den, -1.0);
}
fprintf(ofile,"\n\n");
gsl_vector_free(rho);
gsl_vector_free(tmp);
}
